Using Cloning-GAN Architecture to Unlock the Secrets of Smart Manufacturing: Replication of Cognitive Models

As Industry 4.0 and 5.0 evolve to be highly automated but human-centric, there is a need for process modeling based on digital replicas of physical objects including humans. Knowledge distillation and cognitive cloning offer a way to train operational copies of decision-making black boxes, or donors...

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Bibliographic Details
Published in:Procedia computer science 2024, Vol.232, p.890-902
Main Authors: Terziyan, Vagan, Tiihonen, Timo
Format: Article
Language:English
Subjects:
adversarial distillation
cognitive clones
digital twins
Generative Adversarial Network
knowledge distillation
knowledge transfer
Smart Manufacturing
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Summary:As Industry 4.0 and 5.0 evolve to be highly automated but human-centric, there is a need for process modeling based on digital replicas of physical objects including humans. Knowledge distillation and cognitive cloning offer a way to train operational copies of decision-making black boxes, or donors, without requiring additional data. In this paper, we propose an architecture and analytics for a generative adversarial network, called Cloning-GAN, which enables donor-clone knowledge transfer, including the donor's individual biases. The architecture involves generating challenging samples to be labeled by the donor and used as training data for the clone. We consider several multicriteria requirements for the generated data, including closeness to the decision boundary, uniform distribution in the decision space, maximal confusion for the donor, and challenge for the clone. We present various strategies to balance these conflicting criteria forcing the clone learning quickly the hidden cognitive skills and biases of the donor.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2024.01.089